References

CPD

Climate of the Past Discussions

CPD

1814-9359

Copernicus GmbH

Göttingen, Germany

10.5194/cpd-3-899-2007

Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe

Vrac

¹ Paillard

¹ Naveau

Laboratoire des Sciences du Climat et de l'Environnement, LSCE, CEA/CNRS/UVSQ, Institut Pierre Simon Laplace (IPSL), Centre d'étude de Saclay, Orme des Merisiers, 91191 Gif-Sur-Yvette, France

06 07 2007

3 4 899 933

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The needs of small-scale climate information have become prevalent to study the impacts of future climate change as well as for paleoclimate researches where the reconstructions from proxies are obviously local. In this study we develop a non-linear statistical downscaling method to generate local temperatures and precipitation values from large-scale variables (e.g. Global Circulation Model – GCM – outputs), through Generalized Additive Models (GAMs) calibrated on the present Western Europe climate. First, various monthly GAMs (i.e. one model for each month) are tested for preliminary analysis. Then, annual GAMs (i.e. one model for the 12 months altogether) are developed and tailored for two sets of predictors (geographical and physical) to downscale local temperatures and precipitation. <br><br> As an evaluation of our approach under large-scale conditions different from present Western Europe, projections are realized (1) for present North America and Northern Europe and compared to local observations (spatial test); and (2) for the Last Glacial Maximum (LGM) period, and compared to local reconstructions and GCMs outputs (temporal test). <br><br> In general, both spatial and temporal evaluations indicate that the GAMs are flexible and efficient tools to capture and downscale non-linearities between large- and local-scale variables. More precisely, the results emphasize that, while physical predictors alone are not capable of downscaling realistic values when applied to climate strongly different from the one used for calibration, the inclusion of geographical-type variables – such as altitude, advective continentality and W-slope – into GAM predictors brings robustness and improvement to the method and its local projections.

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